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Welcome to the documentation for the responsible AI toolbox#
The rAI-toolbox is designed to enable methods for evaluating and enhancing both the robustness and the explainability of artificial intelligence (AI) and machine learning (ML) models in a way that is scalable and that composes naturally with other popular ML frameworks.
A key design principle of the rAI-toolbox is that it adheres strictly to the APIs
specified by the PyTorch machine learning framework.
For example, the rAI-toolbox frames the process of solving for an adversarial
perturbation solely in terms of the torch.nn.Optimizer and torch.nn.Module APIs.
This makes it trivial to leverage other libraries and frameworks from the PyTorch
ecosystem to bolster your responsible AI R&D. For instance, one can naturally leverage
the rAI-toolbox together with PyTorch Lightning to
perform distributed adversarial training.
Installation#
To install the basic toolbox, run:
$ pip install rai-toolbox
To include our “mushin” capabilities, which leverage PyTorch Lightning and hydra-zen for enhanced boilerplate-free ML, run:
$ pip install rai-toolbox[mushin]
If instead you want to try out the features in the upcoming version, you can install the latest pre-release of the toolbox with:
$ pip install --pre rai-toolbox
Please refer to the INSTALL_REQUIRES field in
this file
for a list of installation dependencies.
Learning about the responsible AI toolbox#
Our docs are divided into four sections: Tutorials, How-Tos, Explanations, and Reference.
If you want to get a bird’s-eye view of what the rAI-toolbox is all about, or if you are completely new to executing adversarial or explainable AI workflows, check out our Tutorials. For folks who are savvy responsible AI developers, our How-Tos and Reference materials can help acquaint you with the unique capabilities that are offered by the toolbox. Finally, Explanations provide readers with taxonomies, design principles, recommendations, and other articles that will enrich their understanding of rAI-toolbox.
To see some real-world applications of the toolbox, please refer to the examples/ and experiments/ sections of our repository.
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